1. Articles from Shutao Li

    1-8 of 8
    1. Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification

      Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification

      Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions within OCT images are utilized to guide the CNN to achieve more accurate classification. The LACNN simulates the ophthalmologists’ diagnosis that focuses on local lesion-related regions when analyzing the OCT image. Specifically, we ...

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    2. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

      Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

      We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to ...

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    3. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

      Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

      Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following ...

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    4. Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

      Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images

      We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both ...

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    5. 3-D Adaptive Sparsity Based Image Compression with Applications to Optical Coherence Tomography

      3-D Adaptive Sparsity Based Image Compression with Applications to Optical Coherence Tomography

      We present a novel general-purpose compression method for tomographic images, termed 3D adaptive sparse representation based compression (3D-ASRC). In this paper, we focus on applications of 3D-ASRC for the compression of ophthalmic 3D optical coherence tomography (OCT) images. The 3D-ASRC algorithm exploits correlations among adjacent OCT images to improve compression performance, yet is sensitive to preserving their differences. Due to the inherent denoising mechanism of the sparsity based 3D-ASRC, the quality of the compressed images are often better than the raw images they are based on. Experiments on clinical-grade retinal OCT images demonstrate the superiority of the proposed 3D-ASRC over ...

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    6. Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

      Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

      In this paper we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subjectand thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed ...

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    7. Feature Of The Week 6/24/12: Duke University Researchers Develop Techniques for Denoising OCT Images And Publically Share Their Software

      Feature Of The Week 6/24/12: Duke University Researchers Develop Techniques for Denoising OCT Images And Publically Share Their Software

      Duke University researchers have a long history of major advances to the field of Optical Coherence Tomography dating back to some of the first OCT work in the very early 1990s by Dr. Joseph Izatt.  Since the OCT News website started in 2007 Duke has published over 217 papers that appear on OCT News (Link).  One recent example of their work was on “Sparsity Based Denoising of Spectral Domain Optical Coherence Tomography Images”.   Below is a summary of this interesting work and along with a link to software they have developed and made publically available.       -Eric SwansonIn this work, we ...

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    8. Sparsity based denoising of spectral domain optical coherence tomography images

      Sparsity based denoising of spectral domain optical coherence tomography images

      In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority ...

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    1-8 of 8
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    1. (6 articles) Duke University
    2. (6 articles) Sina Farsiu
    3. (4 articles) Joseph A. Izatt
    4. (3 articles) Cynthia A. Toth
    5. (1 articles) University of Melbourne
    6. (1 articles) Anthony N. Kuo
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    Sparsity based denoising of spectral domain optical coherence tomography images Feature Of The Week 6/24/12: Duke University Researchers Develop Techniques for Denoising OCT Images And Publically Share Their Software Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation 3-D Adaptive Sparsity Based Image Compression with Applications to Optical Coherence Tomography Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification Optical Coherence Tomography Angiography in Myopic Patients Quantification of retinal microvasculature and neurodegeneration changes in branch retinal vein occlusion after resolution of cystoid macular edema on optical coherence tomography angiography Machining head for a laser machining device Quantitative Comparison Of Microvascular Metrics On Three Optical Coherence Tomography Angiography Devices In Chorioretinal Disease